Data Analysis on WallStreetBets and Its Impact On the Market
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Github | Linkedin
Topic/Background
I wanted to study the data trends to see if there was an impact to the financial markets from the subreddit group known as “WallStreetBets”.
A brief background on the WallStreetBets subreddit:
- “r/wallstreetbets”, also known as “WallStreetBets” or “WSB”, is a subreddit where participants discuss stock and options trading. It has become notable for its colorful and profane jargon, aggressive trading strategies, and for playing a major role in the GameStop short squeeze that caused losses for some U.S. firms and short sellers in a few days in early 2021 (Wikipedia).
- Roughly 11 million subscribers (at time of posting).
Initial thought:
I wanted to take the data from WallStreetBets and find a pattern of the most popular names mentioned during this period. From there, I would select the most "talked" about names and using financial stock data, to see if there was any correlation with any price movement or trading patterns.
Data Scrape
I decided to scrape historical financial data directly from Yahoo Finance by building a scrapping tool. I did this primarily because I wanted to make sure the data was directly from yahoo finance and specified in a format that I wanted to utilize. Yahoo Finance is one of many websites that contains a large source of trading and financial data. All data is organized by ticker and pulled by specific dates. In addition, data can be organized daily, monthly, or yearly.
An Introduction to the Data
WallStreetBets (WSB) Data:
The period data was taken from 12/28/20 to 10/08/20. I wanted to closely examine the timeline of events relating to the sudden increase in the stock known as Gamestop (GME). This move was unprecedented at the time and was the start of WSB from a simple subreddit into a household name. The data was initially scrapped by Alex Baransky.
Yahoo Finance Data:
To keep things uniform, I took historical stock data pertaining to
Initial Data Analysis
I created this graph to represent and show just how far the outliers were. In this case. the outliers represent the top names mentioned:
I wanted to take the WSB data and first determine the number of "mentions" based on every stock named on the forum for the period.
- Mentions = # of times mentioned in any posts or comments (daily).
- The period was from 12/28/20 – 10/8/21.
- This included positive and negative sentiments.
- Discover which are the top 3 stocks. (in this case it was GME, BB, AMC)
Then I wanted to use another metric to reinforce this initial approach.
- Used daily “positive count” and “negative count” = # of times stock was mentioned in a positive or negative context (using a predictive model).
- I averaged the "% of mentions" for the period to see which names were most widely mentioned in any given day.
- In this case the top names were GME, AMC and NAPA.
- BB was 4th.
NAPA seemed like an outlier so I wanted to examine it more closely. It wasn't a name I heard of before so I dug in a little on the background of the company. I found that the company was a recent IPO and I had a hunch that it was an outlier because it probably had a day with a lot of mentions that skewed the overall mean. I initially assumed it would be around it's IPO date which was in March but this wasn't the case. It was actually in July. Regardless, this made me feel comfortable to discard this name for consideration.
Data on the Top 3 Names
GameStop Corp. (GME)
As we can see here, GME mentions were the highest in January through March of 2020
This corresponds to the volume surge, especially in January and February, and elevated levels in March.
As shown in this graph, most of the price appreciation was done in January of 2020, the first month of the surge of mentions.
AMC Entertainment Holdings (GME)
For AMC, mentions were the highest in February and March of 2020. There is also a slight surge in June.
This corresponds to the volume surge, especially in February, and elevated levels in January, March, and June. (small delay in this case)
Once again most of the price appreciation was in that first month of January.
Blackberry Limited (BB)
For BB, mentions were the highest in January, February, and June of 2020.
This corresponds with the volume move in January, February, and June shown here.
Once again most of the price appreciation was in that first month of January.
Data Findings
There was an increase in overall trading volume activity. There is a potential correlation to the number of mentions in the posts or comments to the actual trading volume of the stocks respectfully.
I suspect that most of the price appreciation was realized in that first initial month. This also aligns with when the number of mentions was first increased. (Notably, AMC was a little delayed). However, the price doesn't appear to move in the same magnitude after the initial move. This makes me conclude that there doesn't appear to be a correlation with the price of the stocks with the number of mentions.
It was also notable to see that the volume and mentions have died down in the past couple of months. Was this a one-time event?
Potential reason why price didn't have any relations with the number of mentions
- Reddit members can be talking about the stock without trading it.
- Some members can potentially be shorting the stock (via puts) or selling their stake (profit-taking).
- The company could take advantage of the stock price and for example, issue more shares which would naturally depress the price of the stock.
- Market capitalization (how big the company is worth by dollar value) increased dramatically after January. This would mean it would be harder to move the stock price because you would need a lot more capital to have the same impact before the price appreciation.
Final Thoughts
I thoroughly enjoyed the process of putting this analysis together from start to finish. It was my first time scrapping data and also my first attempt at handling a large dataset with Python. I definitely think the work done here isn't complete. There is a lot of rework that can be done and hopefully will be able to revisit this project at a later date to further the analysis shown here. Eventually, I hope to build a better understanding if there is any or no relationship at all with WSB and the financial markets.
Sources:
WallStreetBets: www.reddit.com/r/wallstreetbets/
Yahoo Finance: www.finance.yahoo.com/
About Author
